There is a clear need for monitoring dielectric gases and gas mixtures used for insulation and are suppression in high voltage switchgear used throughout the electric utility and related industries. Prescribed levels of gas are required for safe operation of this equipment. A decline in gas level due to gas leakage or other losses must be detected and maintenance activities must be scheduled to restore gas levels or else unsafe conditions or catastrophic equipment failure may result. There are a variety of products commonly called density monitors that have been used by the industry to detect low density levels and provide a system level alarm when gas replenishment is needed. These density monitors typically provide a second level of alarm when gas levels become so low that safe operation of the switchgear is not possible. These density monitor type products are inherently low resolution sensors signaling only a few different level conditions (such as “alarm level” and “lockout level”). Unfortunately, they do not provide the information needed to enable operators to fine tune maintenance and repair schedules and minimize associated operating costs. Further, the environmental cost of gases leaked from such switchgear can be severe. For example, sulfur hexafluoride gas (SF6) is a dielectric gas of choice in widespread use in the industry and is a potent greenhouse gas (GWP 23,900 times that of CO2). The two-level density monitor type products are lacking when it comes to early detection and mitigation of environmentally harmful gas leaks. A gas monitoring system featuring high resolution density measurement and changing density level prediction is a valuable tool for reducing high-voltage switchgear maintenance and repair costs and for detecting gas leaks that pose a danger to the environment. This invention is such a high resolution gas monitoring system that provides these valuable benefits.
Gas density estimates are known to be derivable in a straight-forward fashion from the absolute temperature and the absolute pressure of a gas in a fixed volume using the ideal gas law wherein the density is predicted to be simply proportional to the pressure and inversely proportional to the temperature:
                    pV        =                              nRT            ∴                          n              V                                =                      p            RT                                              (        1        )            where p is absolute pressure, V is volume, n is the gas mass, T is the absolute temperature, and R is the coefficient of proportionality (often referred to as the ideal gas constant).
It is well known that real world gases behave in a way that is more or less non-ideal as the density and temperature of the gas varies. It has been found that a better estimate of the state of a real world gas is available using the so-called virial equation of state:
                              pV          nRT                =                  1          +                                    B              ⁡                              (                T                )                                      ⁢                          n              V                                +                                    C              ⁡                              (                T                )                                      ⁢                                          n                2                                            V                2                                              +          …                                    (        2        )            
The coefficients B(T), C(T), etc. are the so-called first order, second order, etc. virial coefficients respectively and depend upon temperature in a way peculiar to each particular gas or gas mixture. Accurate determination of the absolute pressure and absolute temperature of the monitored gas is still prerequisite to using the virial equation of state to accurately estimate its density.
When a gas sensor is utilized to estimate the gas density in a fixed volume tank of an outdoor high-voltage breaker, the temperatures measured by the gas sensor and the effective temperature of the gas within the tank often diverge significantly over the course of the day (see FIG. 5). For example, over the course of a 180-day test period with a gas sensor applied to a particular high voltage breaker charged with SF6, temperature discrepancies between the sensor temperature measurement and different points on the gas tank ranged from −5.2° C. to +7.9° C. The discrepancy is even larger because the temperature of the gas at different points inside the tank is known to vary even further. Significant temperature gradients arise due to asymmetric heating or cooling due to differential exposure to wind, sun, and precipitation. Localized heating of gas near conductors and contacts carrying variable electrical currents within the tank of electrical equipment is another source of temperature disequilibrium. There is no simple method by which a temperature probe may be positioned so as to measure a temperature that reliably accurately reflects the effective ensemble temperature within a tank, effective ensemble temperature meaning the temperature value that would be observed if the gas system were allowed to come to thermal equilibrium. In the example above, the temperature discrepancies, if unaccounted, lead to density estimate errors ranging from −1.62 to +0.90 kg/m3. Given that the typical operating densities for switchgear charged with SF6 are in the 40 kg/m3 range, the inaccuracy represents a significant percentage error. Accurate gas pressure measurement, while non-trivial, is relatively straight-forward. To summarize, when the effective gas temperature is above the sensor measured temperature (which often arises when the ambient temperature is falling) the density is estimated to be higher than actual. Conversely, when the tank temperature is below the sensor temperature (when ambient temperature is rising typically) the density is estimated to be lower than actual. An example depicting this phenomenon is shown in FIG. 5 and described below.